AUDITING WITH AI: A THEORETICAL FRAMEWORK FOR APPLYING MACHINE LEARNING ACROSS THE INTERNAL AUDIT LIFECYCLE

Abstract

Artificial Intelligence (hereinafter AI), and specifically, Machine Learning (hereinafter, ML), has shown tremendous potential to revolutionize the internal audit (hereinafter, IA) profession, from enabling audit coverage of entire test populations, to introducing objectivity in the analysis of key areas. However, prior literature shows that the multiplicity of innovation options can be overwhelming. This paper aims to offer a theoretical framework that would enable audit practitioners, within both industry and professional services, to consider how ML capabilities can be harnessed to their fullest potential across the internal audit lifecycle, from audit planning to reporting. The paper discusses how DA and ML capabilities relate to the internal audit function’s (hereinafter, IAF) remit, drawing from extant literature. In doing so, the paper identifies the most specific options available to IAFs to drive innovation across each segment of the audit lifecycle, leveraging various DA and ML techniques, and supports the assertion that auditors require a continuous innovation mind-set to be effective change agents. The paper also draws out the requirement for effective guardrails, especially with emerging technology, such as Generative AI. Finally, the paper discusses how the value arising from these efforts can be measured by IAFs.